That we are awash in information is an often used, though certainly correct axiom of the early 21st century. The ubiquity of information access portals in everyday life provides the potential for connecting meaning to any experience or data set. Potential meaning is the key tenant here. How information sets are evaluated sorted and searched, remains the ultimate determinant of their actual value to the user.
Information in this modern sense may be considered as a two component entity; the first being the actual data it embodies, the second being its accessibility, based on identifying tags, anchors, or fields.
The exponential increases in both data production and storage capabilities have matched, not surprisingly, very well since “Moore's Law” tales have circulated through computational communities. Data search technologies, how information is sorted and accessed, however, have experienced a much more varied history. Though the success of market leaders' search algorithms, such as Google's “page-rank,” belie their effectiveness, the increasing volume and complexity of modern information structure has lead to increased user dissatisfaction and frustration. Three significant points of current search algorithm dissatisfaction are search output discrepancies, search output bias, and an incongruous match of search interface with brain heuristic functioning.
Search output discrepancies may result from a circumstance referred to as the “local search problem.” This problem arises when global data sets containing extrinsic information are not consistently cross checked or “curated” with local data sets containing intrinsic information. For example, a search for vacation destinations may yield inconsistent output if not updated with local information such as prices, business hours and patron ratings.
As search engine and social networking leaders battle the concept of truth and validity on the internet, a potential problem looms for society as a whole. The popularity of social networking sites has made searching within peer preference databases very effective and appealing. A search conducted within a social network database consisting of peers with similar preferences (intrinsic data) is highly likely to produce user preferable results. Such a behavior (searching within closed data sets), however, limits variation by culling preference outliers. As in biology, any system lacking diversity, while successful within its native context, is resistant to change, slow to adapt, and quickly expends its resources.
Search output bias may result primarily from either (1) discontinuities in search parameters weightings between the user and the search algorithm, or (2) misguiding parameter weightings through “search engine optimization” practices. In either case, miscommunication or lack of clear communication between user input and search algorithm programming may skew output away from the user's intentions.
Addressing the third point of search algorithm dissatisfaction, search is a learning process. Psychological studies of our mind's processing methods maintain that we gather, interpret, and organize stimuli based on heuristic, information seeking behaviors. Schemas, or information frameworks, drive these behaviors and are in turn reshaped in a feedback relationship with them. Schemas contain impressions and rules of thumb that are based on collective experiences and importantly are unique to the individual. Schemas, then, ultimately define the individualistic nature of understanding within our species. Given this perspective of understanding, and for the purpose of the present discussion on search, a concept—something thought to be “understood”—may be compared to what is known as an object in programming languages, such as JavaScript. An object's significance, in this sense, is determined by parameters such as “data type”, “length”, “location”, etc.
On the output side of current search practices, in seeking to model the effectiveness of this heuristic-schema relationship, today's search algorithms employ data frequency, data proximity, and hardcoded database information to create and shape accurate results. These strategies do result in heuristic-like search behavior, though the schema upon which they rely are ultimately based on most common or most statistically probable search parameter to search result relationships, not on the user's experiential knowledge or contextual intent.
The input side of current search interfaces interact poorly with the processing methodology of our brain. Currently, search is guided by a serial step-by-step process. The user attempts to convert a search concept to a string of terms (hoping to characterize the concept), enters the terms, sorts through the search output, and possibly re-enters terms. This strategy works well for navigational searches where pre-conceptualized output, such as a web page or a phone number, is sought. For informational searches, such as for “good restaurants”, “comfortable apartments”, or “a person like this friend”, current search interfaces fall far short of providing timely accurate output for the user. Recent studies have shown that informational searches and the length of search queries for them are growing more rapidly than any other type of search.
In order to match our minds' organized discovery process and experiential knowledge structure, why not allow for the input and modeling of the search concept itself? For example, if searching for a “person like a friend”, why not use the friend itself as the object that drives the search? The search object could be represented as a coordinate system whose boundary is composed of parameters defining the object (e.g. a friend's parameters might be “gender”, “age”, etc.). Given this representation, the magnitude and direction of vectors connecting object parameters would translate to a weight or significance of that parameter. Collectively all parameters, weighted appropriately, would compose the object's search query string. Additionally, manually manipulating a parameter location within the coordinate space could adjust its relative weight in the overall search query string.
Through this process, search would more closely match the mind's heuristic behavior of obtaining, classifying, and presenting information. It would allow a more intuitive graphical representation of the multi-dimensional information structure used to drive decision making processes. It should speed the search process by maintaining a multi-dimensional view of the critical characteristics that are intended to guide the search. It may also enable a more exhaustive search due to the graphical sensitivity.
There exists an apparent need for an interface between user and search algorithms which would allow the joining of discontinuous data sets, an intuitive means of user awareness and manipulation of search parameter weightings, as well as an effective means of searching across intrinsic and extrinsic data sets.
Finally, the search interface described herein provides an intuitive and fundamentally more accurate means for image search. As today's information schema become increasingly graphically structured, a need arises to be able to search within this graphical landscape. Currently, image search requires a series of translations, such as from user cognition to language representation, from language representation to image meta-tag searching, and from image meta-tag searching to search results structuring. Each one of these translational steps introduce a degree of error, distancing the original intent from search output. From an operational perspective, this disconnect between intent and output introduce an unaddressed need at two levels. One, the user either is not connected with the most accurate solution to their search, or must take additional time to search. Two, information providers, observing user search behavior, will misinterpret the user's behavior-intent relationship, and thus inaccurately addressing the user's needs or preferences.